A radiofrequency ablation modulation method and system based on multi-parameter fusion and real-time feedback

Abstract

This invention discloses a radiofrequency ablation adjustment method and system based on multi-parameter fusion and real-time feedback, belonging to the field of radiofrequency ablation technology. By acquiring various physiological signal data, it studies real-time tissue temperature in isolation, introducing the participation of electrical signals to derive accurate cell water loss rate inversion values, thus providing more precise calculation data and ensuring more accurate damage assessment results. Furthermore, it utilizes a pre-set tissue damage assessment prediction model to predict tissue damage based on the cell water loss rate inversion values, eliminating the need for complex model updates and training, greatly improving the efficiency of enhanced tissue damage assessment. Moreover, it calculates real-time cell water loss rate using real-time physiological signal data, using it as the core damage criterion for damage assessment, and generates corresponding real-time radiofrequency ablation adjustment operation commands based on the specific damage assessment results, forming an integrated control system from parameter measurement to adjustment feedback.

Description

Technical Field

[0001] This invention belongs to the field of radiofrequency ablation technology, specifically relating to a radiofrequency ablation adjustment method and system based on multi-parameter fusion and real-time feedback. Background Technology

[0002] Radiofrequency ablation (RFA), a minimally invasive thermotherapy technique, uses high-frequency alternating current to generate ion oscillations and frictional heat in the target tissue, raising the local temperature to 50-100℃ to induce irreversible cell necrosis. It is widely used in the treatment of diseases such as tumors, arrhythmias, and chronic pain.

[0003] However, existing ablation systems still face multiple technical bottlenecks in clinical applications. The core issue lies in the insufficient quantitative perception of the dynamic evolution of the thermal field and the lack of real-time control mechanisms. Traditional systems mainly rely on preset fixed parameter combinations (such as power, time, and electrode temperature thresholds) for empirical operation. Although they can indirectly reflect tissue status through independent monitoring of electrical parameters such as voltage, current, and resistance, these parameters only present local physical quantity changes and lack a direct mapping relationship with the degree of tissue damage (such as changes in cell membrane permeability and the extent of collagen denaturation). For example, a decrease in resistance may simultaneously correspond to two completely different pathological processes: tissue dehydration and carbonization (sudden drop in impedance) and expansion of the effective ablation zone (extracellular fluid leakage), which existing systems cannot distinguish. More importantly, the dynamic process of cellular structural changes (such as mitochondrial swelling and cytoskeleton breakage) during ablation cannot be perceived in real time, forcing physicians to assess the effect only after ablation through imaging or pathological examination, creating a closed-loop delay between operation and assessment. Furthermore, the data processing cycle of existing feedback control algorithms generally exceeds 100 milliseconds, lagging far behind the thermal diffusion rate (approximately 1-2 mm / s). This results in power adjustment often being in a catch-up response state, making it difficult to achieve precise energy deposition control. This technical deficiency directly leads to a persistently high incidence of clinical complications—statistics show that events of excessive tissue carbonization (>100℃) or insufficient ablation (<50℃) caused by the lack of coordinated temperature-impedance-power control account for as much as 18%, severely restricting the safety and effectiveness of radiofrequency ablation technology.

[0004] As mentioned above, how to provide a radiofrequency ablation modulation method and system based on multi-parameter fusion and real-time feedback that can enhance the effectiveness and accuracy of damage assessment and form an integrated control from parameter measurement to regulation feedback has become an important research topic in this field. Summary of the Invention

[0005] The purpose of this invention is to provide a radiofrequency ablation adjustment method and system based on multi-parameter fusion and real-time feedback, so as to solve the above-mentioned problems existing in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] In a first aspect, the present invention provides a radiofrequency ablation modulation method based on multi-parameter fusion and real-time feedback, comprising:

[0008] Real-time physiological signal data is acquired, and feature calculations are performed based on the real-time physiological signal data to obtain real-time physiological feature data, wherein the real-time physiological signal data includes real-time physiological temperature signal parameters and real-time physiological electrical signal parameters;

[0009] The real-time physiological characteristic data is inverted to calculate the first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter. The first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter are fused to use the fused cell water loss rate parameter as the real-time cell water loss rate inversion value.

[0010] A preset tissue damage assessment and prediction model is obtained, and the real-time physiological characteristic data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment and prediction model so as to calculate the tissue damage assessment value through the tissue damage assessment and prediction model.

[0011] The target adjustment conditions are obtained, and the real-time physiological characteristic data, the real-time cell water loss rate inversion value, and the tissue damage assessment value are judged based on the target adjustment conditions to obtain the judgment result. The corresponding radiofrequency ablation real-time adjustment operation command is generated and fed back according to the judgment result to complete the real-time adjustment of radiofrequency ablation.

[0012] In one possible design, real-time physiological signal data is acquired, and feature calculations are performed based on the real-time physiological signal data to obtain real-time physiological feature data, including:

[0013] Real-time physiological temperature signal parameters and real-time physiological electrical signal parameters are acquired and integrated into real-time physiological signal data. The real-time physiological temperature signal parameters include real-time tissue temperature, and the real-time physiological electrical signal parameters include real-time voltage RMS value, real-time current RMS value, and real-time impedance amplitude.

[0014] The phase difference between the real-time voltage RMS value and the real-time current RMS value is calculated to obtain the real-time voltage-current phase difference.

[0015] The real-time active power is calculated using the real-time effective voltage value, the real-time effective current value, and the real-time voltage-current phase difference.

[0016] Obtain the preset sampling period and the tissue temperature at the previous moment. Calculate the tissue temperature change value based on the real-time tissue temperature and the tissue temperature at the previous moment. Use the ratio of the tissue temperature change value to the sampling period as the tissue temperature change rate at the current moment, and use the tissue temperature change rate at the current moment as the real-time tissue temperature change rate.

[0017] Based on the real-time impedance amplitude, the real-time conductivity is calculated;

[0018] The real-time impedance amplitude, the real-time voltage and current phase difference, the real-time active power, the real-time tissue temperature change rate, and the real-time conductivity are integrated to form real-time physiological characteristic data.

[0019] In one possible design, the real-time physiological characteristic data is inverted to calculate a first cell water loss rate parameter, a second cell water loss rate parameter, and a third cell water loss rate parameter, including:

[0020] Obtain the preset tissue energy absorption coefficient and initial voltage-current phase difference. Based on the real-time voltage-current phase difference in the real-time physiological characteristic data, calculate the first cell water loss rate parameter ηφ using the following formula:

[0021]

[0022] Where φ0 is the initial voltage-current phase difference, φ is the real-time voltage-current phase difference, and k1 is the preset tissue energy absorption coefficient;

[0023] Obtain the preset sensitivity coefficient and baseline impedance for the linear correlation between impedance and water loss rate. Based on the real-time impedance amplitude in the real-time physiological characteristic data, calculate the second cell water loss rate parameter η using the following formula. R :

[0024]

[0025] Where R0 is the basic impedance, R is the real-time impedance amplitude, and α is the sensitivity coefficient for the linear correlation between impedance and water loss rate.

[0026] Obtain the preset conductivity-water loss rate correlation attenuation coefficient and baseline conductivity. Based on the real-time conductivity in the real-time physiological characteristic data, calculate the third cell water loss rate parameter η using the following formula. σ :

[0027]

[0028] Where σ0 is the basic conductivity, σ is the real-time conductivity, and γ is the conductivity-water loss rate correlation attenuation coefficient.

[0029] In one possible design, the first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter are fused to obtain a fused cell water loss rate parameter as a real-time cell water loss rate inversion value, including:

[0030] The preset sensitivity of the dynamic dominant weight switching of electrical parameters is obtained. Based on the real-time tissue temperature change rate in the real-time physiological characteristic data, the real-time dominant weight value ω of the electrical parameters at the current moment is calculated using the following formula. ：

[0031]

[0032] Where δ is the real-time tissue temperature change rate, k ω The sensitivity of dynamic dominant weight switching is given by e, where e is the base of the natural logarithm, and the electrical parameters include the first cell water loss rate parameter η. φ and the second cell water loss parameter η R ;

[0033] Obtain a preset first parameter confidence level and a second parameter confidence level, wherein the first parameter confidence level is used to represent the confidence level of the first cell water loss rate parameter η. φ The degree of trust in the second parameter, η, is used to represent the confidence level of the second cell water loss rate parameter. R The level of trust;

[0034] Based on the dominant weight values ​​of the real-time electrical parameters, the confidence levels of the first parameter and the second parameter, the weight ω of the first parameter is calculated using the following formula. φ The second parameter weight ω R and the third parameter weight ω σ :

[0035]

[0036] Among them, C φ C represents the confidence level of the first parameter. R The confidence level is the second parameter.

[0037] Using the first parameter weight ω φ The second parameter weight ω R and the third parameter weight ω σ The water loss rate parameter η of the first cell is calculated using the following formula. φ The second cell water loss rate parameter η R and the third cell water loss rate parameter η σ Perform parameter fusion:

[0038] η fused =ωφ ·η φ +ω R ·η R +ω σ ·η σ (6)

[0039] Where, η fused For parameters related to the water loss rate of fused cells;

[0040] The fused cell water loss rate parameter ηfused is determined as the real-time cell water loss rate inversion value.

[0041] In one possible design, a preset tissue damage assessment and prediction model is obtained. The real-time physiological characteristic data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment and prediction model to calculate the tissue damage assessment value, including:

[0042] Obtain a preset tissue damage assessment and prediction model, wherein the tissue damage assessment and prediction model includes a thermal damage prediction model and an ablation depth prediction model;

[0043] Obtain the total accumulated energy of the tissue at the previous moment, and calculate the total accumulated energy of the tissue at the current moment based on the total accumulated energy of the tissue at the previous moment and the real-time active power in the real-time physiological characteristic data;

[0044] Using the aforementioned thermal damage prediction model, the predicted thermal damage value Ω is calculated using the following formula. ：

[0045]

[0046] Where A is the preset molecular collision rate, and E a As a preset protein denaturation barrier, R g Let t be the gas constant, T be the tissue temperature, t be the current time, τ be the time variable, T(τ) be the function of tissue temperature changing with time, and e be the base of the natural logarithm.

[0047] Obtain a preset thermal damage determination threshold, and compare the predicted thermal damage value Ω with the thermal damage determination threshold;

[0048] If the predicted thermal damage value Ω does not exceed the thermal damage determination threshold, the determination result is that no thermal damage has occurred.

[0049] If the predicted thermal damage value Ω exceeds the thermal damage determination threshold, the determination result is that thermal damage has occurred. The total energy accumulated in the tissue at the current moment and the real-time cell water loss rate inversion value are used as inputs to the ablation depth prediction model. The ablation depth prediction value D is then calculated using the ablation depth prediction model according to the following formula. ：

[0050]

[0051] Where k1 is the preset tissue energy absorption coefficient, k2 is the preset water loss rate influencing factor, and E total The total energy accumulated by the tissue at the current moment, ηfused is the water loss rate parameter of the fused cells, and e is the base of the natural logarithm;

[0052] The obtained judgment result and the ablation depth prediction value D are integrated to form a tissue damage assessment value.

[0053] In one possible design, a target conditioning condition is obtained, and the real-time physiological characteristic parameters, the real-time cell water loss rate inversion value, and the tissue damage assessment value are judged based on the target conditioning condition to obtain a judgment result, including:

[0054] Acquire target conditioning conditions, wherein the target conditioning conditions include tissue temperature conditioning conditions, cell water loss rate conditioning conditions, and tissue damage conditioning conditions;

[0055] Based on the tissue temperature regulation conditions, the real-time tissue temperature is judged to obtain a first judgment result;

[0056] Based on the cell water loss rate adjustment conditions, the real-time cell water loss rate inversion value is judged to obtain a second judgment result;

[0057] Based on the tissue damage adjustment conditions, the tissue damage assessment value is judged to obtain a third judgment result;

[0058] The first judgment result, the second judgment result, and the third judgment result are integrated to obtain a judgment result;

[0059] In one possible design, a corresponding real-time adjustment command for radiofrequency ablation is generated and fed back based on the judgment result to complete the real-time adjustment of radiofrequency ablation, including:

[0060] Obtain a preset adjustment operation table, and match the corresponding radiofrequency ablation real-time adjustment operation instruction in the adjustment operation table according to the judgment result;

[0061] The radiofrequency ablation real-time adjustment operation command is fed back to the radiofrequency device to control the radiofrequency device to execute the radiofrequency ablation real-time adjustment operation command and adjust the input parameters of radiofrequency ablation in real time.

[0062] Secondly, the present invention provides a radiofrequency ablation intensity adjustment system based on multi-parameter fusion and real-time feedback, applied to the radiofrequency ablation adjustment method based on multi-parameter fusion and real-time feedback as described in the first aspect or any possible design of the first aspect, comprising:

[0063] The signal acquisition unit is used to acquire real-time physiological signal data, and perform feature calculations based on the real-time physiological signal data to obtain real-time physiological feature data, wherein the real-time physiological signal data includes real-time physiological temperature signal parameters and real-time physiological electrical signal parameters.

[0064] The water loss rate calculation unit is used to perform inversion calculation on the real-time physiological characteristic data, calculate the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter, and perform parameter fusion on the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter, so as to use the fused cell water loss rate parameter as the real-time cell water loss rate inversion value.

[0065] The damage assessment unit is used to obtain a preset tissue damage assessment prediction model. The real-time physiological characteristic data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment prediction model to calculate the tissue damage assessment value.

[0066] The feedback adjustment unit is used to acquire target adjustment conditions, judge the real-time physiological characteristic data, the real-time cell water loss rate inversion value and the tissue damage assessment value based on the target adjustment conditions, obtain the judgment result, and generate and feed back the corresponding radiofrequency ablation real-time adjustment operation command based on the judgment result to complete the real-time adjustment of radiofrequency ablation.

[0067] Thirdly, the present invention provides an electronic device comprising a memory, a processor, and a transceiver connected in sequence and communication, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the radio frequency ablation modulation method based on multi-parameter fusion and real-time feedback as described in the first aspect or any possible design of the first aspect.

[0068] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, perform the radio frequency ablation modulation method based on multi-parameter fusion and real-time feedback as described in the first aspect or any possible design of the first aspect.

[0069] Fifthly, the present invention provides a computer program product containing instructions that, when the instructions are executed on a computer, cause the computer to perform the radiofrequency ablation modulation method based on multi-parameter fusion and real-time feedback as described in the first aspect or any possible design of the first aspect.

[0070] Beneficial Effects: This invention provides a radiofrequency ablation modulation method and system based on multi-parameter fusion and real-time feedback. First, real-time physiological signal data is acquired. Based on this data, feature calculations are performed to obtain real-time physiological feature data, including real-time physiological temperature signal parameters and real-time physiological electrical signal parameters. Second, the real-time physiological feature data undergoes inversion calculation to calculate a first cell water loss rate parameter, a second cell water loss rate parameter, and a third cell water loss rate parameter. These parameters are then fused to generate a fused parameter. The fusion cell water loss rate parameter is used as the real-time cell water loss rate inversion value; then, a preset tissue damage assessment prediction model is obtained, and the real-time physiological feature data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment prediction model to calculate the tissue damage assessment value; finally, target adjustment conditions are obtained, and the real-time physiological feature data, the real-time cell water loss rate inversion value and the tissue damage assessment value are judged by the target adjustment conditions to obtain the judgment result, and the corresponding radiofrequency ablation real-time adjustment operation command is generated and fed back according to the judgment result to complete the real-time adjustment of radiofrequency ablation. By acquiring various physiological signal data, this study not only focuses on real-time tissue temperature but also incorporates electrical signals to derive accurate cell dehydration rate inversion values. This achieves cell-level sensing precision, providing more accurate computational data and ensuring more precise damage assessment results. Furthermore, a pre-defined tissue damage assessment prediction model is used to predict tissue damage based on the cell dehydration rate inversion values, eliminating the need for complex model updates and training. This significantly improves the efficiency of enhanced tissue damage assessment. Additionally, real-time cell dehydration rate is calculated using real-time physiological signal data and used as the core damage criterion for damage assessment. Based on the specific damage assessment results, corresponding real-time radiofrequency ablation adjustment commands are generated, forming an integrated control system from parameter measurement to adjustment feedback. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating the radiofrequency ablation adjustment method based on multi-parameter fusion and real-time feedback provided in an embodiment of the present invention.

[0072] Figure 2A functional structure diagram of a radiofrequency ablation adjustment system based on multi-parameter fusion and real-time feedback provided in an embodiment of the present invention;

[0073] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0074] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the present invention will be briefly introduced below in conjunction with the accompanying drawings and descriptions of the embodiments or the prior art. Obviously, the following description of the structure of the accompanying drawings is only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort. It should be noted that the description of these embodiments is for the purpose of helping to understand the present invention, but does not constitute a limitation of the present invention.

[0075] It should be understood that although the terms first, second, etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are only used to distinguish one unit from another. For example, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit, without departing from the scope of the exemplary embodiments of the invention.

[0076] It should be understood that the term "and / or" that may appear in this document is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can mean: A exists alone, B exists alone, and A and B exist simultaneously. The term " / and" that may appear in this document describes another relationship between related objects, indicating that two relationships can exist. For example, A / and B can mean: A exists alone, and A and B exist alone. In addition, the character " / " that may appear in this document generally indicates that the related objects before and after it are in an "or" relationship.

[0077] Example:

[0078] like Figure 1 As shown, the first aspect of this embodiment provides a radiofrequency ablation adjustment method based on multi-parameter fusion and real-time feedback, which may include, but is not limited to, the following steps:

[0079] S1. Acquire real-time physiological signal data, and perform feature calculations based on the real-time physiological signal data to obtain real-time physiological feature data, wherein the real-time physiological signal data includes real-time physiological temperature signal parameters and real-time physiological electrical signal parameters;

[0080] In one possible implementation, step S1, acquiring real-time physiological signal data, and performing feature calculations based on the real-time physiological signal data to obtain real-time physiological feature data, can be decomposed into, but is not limited to, the following steps S11-S16, including:

[0081] S11. Real-time acquisition of real-time physiological temperature signal parameters and real-time physiological electrical signal parameters, and integration of the real-time physiological temperature signal parameters and the real-time physiological electrical signal parameters into real-time physiological signal data, wherein the real-time physiological temperature signal parameters include real-time tissue temperature, and the real-time physiological electrical signal parameters include real-time voltage RMS value, real-time current RMS value, and real-time impedance amplitude;

[0082] S12. Calculate the phase difference between the real-time voltage RMS value and the real-time current RMS value to obtain the real-time voltage-current phase difference;

[0083] S13. Calculate the real-time active power using the real-time voltage RMS value, the real-time current RMS value, and the real-time voltage-current phase difference;

[0084] S14. Obtain the preset sampling period and the tissue temperature at the previous moment. Calculate the tissue temperature change value based on the real-time tissue temperature and the tissue temperature at the previous moment. Use the ratio of the tissue temperature change value to the sampling period as the tissue temperature change rate at the current moment, and use the tissue temperature change rate at the current moment as the real-time tissue temperature change rate.

[0085] S15. Calculate the real-time conductivity based on the real-time impedance amplitude;

[0086] S16. Integrate the real-time impedance amplitude, the real-time voltage and current phase difference, the real-time active power, the real-time tissue temperature change rate, and the real-time conductivity to form real-time physiological characteristic data.

[0087] It should be noted that the real-time effective voltage, real-time effective current, and real-time impedance amplitude required in this embodiment can only be obtained by acquiring the basic voltage and current through signal acquisition equipment and preprocessing them. The real-time voltage and current phase difference needs to be obtained by acquiring the voltage and current changes for a whole cycle according to the sampling period and performing phase calculation. The calculation of real-time active power also needs to be calculated by multiplying the cosine value of the real-time voltage and current phase difference. The "effective" and "active" mentioned here refer to the clinical test data obtained.

[0088] S2. Perform inversion calculation on the real-time physiological characteristic data to calculate the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter, and fuse the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter to use the fused cell water loss rate parameter as the real-time cell water loss rate inversion value;

[0089] In one possible implementation, step S2 involves inverting the real-time physiological characteristic data to calculate the first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter. This step can be, but is not limited to, decomposed into the following steps S21-S23, including:

[0090] S21. Obtain the preset tissue energy absorption coefficient and initial voltage-current phase difference. Based on the real-time voltage-current phase difference in the real-time physiological characteristic data, calculate the first cell water loss rate parameter ηφ using the following formula:

[0091]

[0092] Where φ0 is the initial voltage-current phase difference, φ is the real-time voltage-current phase difference, and k1 is the preset tissue energy absorption coefficient;

[0093] S22. Obtain the preset sensitivity coefficient and baseline impedance for the linear correlation between impedance and water loss rate. Based on the real-time impedance amplitude in the real-time physiological characteristic data, calculate the second cell water loss rate parameter η using the following formula. R :

[0094]

[0095] Where R0 is the basic impedance, R is the real-time impedance amplitude, and α is the sensitivity coefficient for the linear correlation between impedance and water loss rate.

[0096] S23. Obtain the preset conductivity-water loss rate correlation attenuation coefficient and baseline conductivity. Based on the real-time conductivity in the real-time physiological characteristic data, calculate the third cell water loss rate parameter η using the following formula. σ :

[0097]

[0098] Where σ0 is the basic conductivity, σ is the real-time conductivity, and γ is the conductivity-water loss rate correlation attenuation coefficient.

[0099] In one possible implementation, step S2 involves fusing the first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter to obtain the fused cell water loss rate parameter as the real-time cell water loss rate inversion value. This step can be, but is not limited to, decomposed into the following steps S24-S28, including:

[0100] S24. Obtain the preset electrical parameter dynamic dominant weight switching sensitivity, and calculate the real-time electrical parameter dominant weight value ω at the current moment according to the real-time tissue temperature change rate in the real-time physiological characteristic data using the following formula. ：

[0101]

[0102] Where δ is the real-time tissue temperature change rate, k ω The sensitivity of dynamic dominant weight switching is given by e, where e is the base of the natural logarithm, and the electrical parameters include the first cell water loss rate parameter η. φ and the second cell water loss parameter η R ;

[0103] S25. Obtain a preset first parameter confidence level and a second parameter confidence level, wherein the first parameter confidence level is used to represent the confidence level of the first cell water loss rate parameter η. φ The degree of trust in the second parameter, η, is used to represent the confidence level of the second cell water loss rate parameter. R The level of trust;

[0104] S26. Based on the dominant weight value of the real-time electrical parameter, the confidence level of the first parameter, and the confidence level of the second parameter, the weight ω of the first parameter is calculated using the following formula. φ The second parameter weight ω R and the third parameter weight ω σ :

[0105]

[0106] Among them, C φ C represents the confidence level of the first parameter. R The confidence level is the second parameter.

[0107] S27. Using the first parameter weight ω φ The second parameter weight ω R and the third parameter weight ω σ The water loss rate parameter η of the first cell is calculated using the following formula. φ The second cell water loss rate parameter η R and the third cell water loss rate parameter η σ Perform parameter fusion:

[0108] η fused =ω φ ·η φ +ω R ·η R +ω σ ·η σ (6)

[0109] Where, η fused For parameters related to the water loss rate of fused cells;

[0110] S28. The water loss rate parameter η of the fused cells generated by the fusion is... fused It was determined to be the inversion value of real-time cell water loss rate.

[0111] It should be noted that this embodiment performs multi-dimensional calculations of the cell water loss rate parameter. This is because, in the electrical dimension (the first cell water loss rate parameter η), φ Second cell water loss parameter η R ) and conductivity dimension (third cell water loss parameter η) σ The calculated cell water loss rate results are all partial results. Different stages of radiofrequency ablation have different effects. If only one parameter is considered, the cell water loss rate cannot be obtained comprehensively and accurately, and cell carbonization cannot be accurately controlled. Only by fully considering this influence and performing appropriate parameter fusion can a more accurate cell water loss rate inversion calculation result be obtained. Specifically, if the real-time tissue temperature change rate is greater than 0, it indicates that the current stage is the heating phase of radiofrequency ablation. At this time, the actual cell water loss rate is calculated mainly by electrical parameters. When the real-time tissue temperature change rate approaches 0, it indicates that the current stage is the steady-state phase of radiofrequency ablation. At this time, the actual cell water loss rate is calculated by using electrical parameters and conductivity parameters with almost equal weight.

[0112] The real-time electrical parameter dominant weight value is introduced to measure the dominance of electrical and conductivity parameters. It does not require external adjustment; the weights are adaptively adjusted based on real-time tissue temperature to complete the weight allocation and form the first parameter weight ω. φ The second parameter weight ω R and the third parameter weight ω σ This ensures that accurate real-time cell dehydration rate inversion calculation results are available at each stage of radiofrequency ablation.

[0113] S3. Obtain a preset tissue damage assessment prediction model, and input the real-time physiological characteristic data and the real-time cell water loss rate inversion value as input quantities into the tissue damage assessment prediction model to calculate the tissue damage assessment value through the tissue damage assessment prediction model.

[0114] In one possible implementation, step S3 involves obtaining a preset tissue damage assessment prediction model, using the real-time physiological characteristic data and the real-time cell water loss rate inversion value as inputs to the tissue damage assessment prediction model, and calculating the tissue damage assessment value through the tissue damage assessment prediction model. This can be broken down into, but is not limited to, the following steps S31-S37, including:

[0115] S31. Obtain a preset tissue damage assessment and prediction model, wherein the tissue damage assessment and prediction model includes a thermal damage prediction model and an ablation depth prediction model;

[0116] S32. Obtain the total accumulated energy of the tissue at the previous moment, and calculate the total accumulated energy of the tissue at the current moment based on the total accumulated energy of the tissue at the previous moment and the real-time active power in the real-time physiological characteristic data;

[0117] S33. Using the aforementioned thermal damage prediction model, the predicted thermal damage value Ω is calculated using the following formula. ：

[0118]

[0119] Where A is the preset molecular collision rate, and E a As a preset protein denaturation barrier, R g Let t be the gas constant, T be the tissue temperature, t be the current time, τ be the time variable, T(τ) be the function of tissue temperature changing with time, and e be the base of the natural logarithm.

[0120] S34. Obtain a preset thermal damage determination threshold, and compare the predicted thermal damage value Ω with the thermal damage determination threshold;

[0121] S35. If the predicted thermal damage value Ω does not exceed the thermal damage determination threshold, the determination result is that no thermal damage has occurred.

[0122] S36. If the predicted thermal damage value Ω exceeds the thermal damage determination threshold, the determination result is that thermal damage has occurred. The total energy accumulated in the tissue at the current moment and the real-time cell water loss rate inversion value are used as inputs to the ablation depth prediction model. The ablation depth prediction value D is calculated using the ablation depth prediction model according to the following formula. ：

[0123]

[0124] Where k1 is the preset tissue energy absorption coefficient, k2 is the preset water loss rate influencing factor, and E totalThe total energy accumulated by the tissue at the current moment, ηfused is the water loss rate parameter of the fused cells, and e is the base of the natural logarithm;

[0125] S37. Integrate the obtained determination result and the ablation depth prediction value D to form a tissue damage assessment value.

[0126] It should be noted that this embodiment directly sets a fixed calculation method in the tissue damage assessment and prediction model, eliminating the need for model updates and adjustments based on complex historical data, as well as complex network layer design and calculations. This greatly alleviates computational pressure, reduces computing power requirements, and improves the applicability of the method in this embodiment. Furthermore, by avoiding complex inter-network layer operations and processing, the calculation speed of this embodiment is improved, and a two-layer model is set up for data filtering and calculation. Specifically, this embodiment includes, but is not limited to, a thermal damage prediction model and an ablation depth prediction model. The thermal damage prediction model is used to qualitatively judge tissue damage based on the predicted thermal damage value Ω. If the obtained thermal damage prediction value Ω does not exceed the preset thermal damage judgment threshold, it can be considered that no thermal damage has occurred. In this case, the judgment result can be directly returned to form a tissue damage assessment value of 0, and no radiofrequency ablation adjustment is required. If the obtained thermal damage prediction value Ω exceeds the preset thermal damage judgment threshold, it can be considered that thermal damage has occurred. In this case, the real-time cell water loss rate inversion value is used as an input to the ablation depth prediction model to calculate the ablation depth prediction value D. The ablation depth prediction model is used to quantitatively judge tissue damage based on the real-time cell water loss rate inversion value. It is only calculated when thermal damage is determined to have occurred. This design also reduces the amount of calculation and improves the efficiency of damage assessment.

[0127] S4. Obtain target adjustment conditions, and judge the real-time physiological characteristic data, the real-time cell water loss rate inversion value and the tissue damage assessment value through the target adjustment conditions to obtain the judgment result, and generate and feed back the corresponding radiofrequency ablation real-time adjustment operation command according to the judgment result to complete the real-time adjustment of radiofrequency ablation.

[0128] In one possible implementation, step S4 involves obtaining target adjustment conditions, and using these conditions to evaluate the real-time physiological characteristic parameters, the real-time cell water loss rate inversion value, and the tissue damage assessment value to obtain a judgment result. This step can be broken down into, but is not limited to, the following steps S41-S45, including:

[0129] S41. Obtain target conditioning conditions, wherein the target conditioning conditions include tissue temperature conditioning conditions, cell water loss rate conditioning conditions, and tissue damage conditioning conditions;

[0130] S42. Based on the tissue temperature regulation conditions, determine the real-time tissue temperature to obtain a first determination result;

[0131] S43. Based on the cell water loss rate adjustment conditions, the real-time cell water loss rate inversion value is judged to obtain a second judgment result;

[0132] S44. Based on the tissue damage adjustment conditions, the tissue damage assessment value is judged to obtain a third judgment result;

[0133] S45. Integrate the first judgment result, the second judgment result, and the third judgment result to obtain a judgment result.

[0134] In one possible implementation, step S4, generating and feeding back a corresponding real-time radiofrequency ablation adjustment command based on the judgment result to complete the real-time adjustment of radiofrequency ablation, can be, but is not limited to, decomposed into the following steps S46-S47, including:

[0135] S46. Obtain a preset adjustment operation table, and match the corresponding radiofrequency ablation real-time adjustment operation instruction in the adjustment operation table according to the judgment result;

[0136] S47. Feedback the real-time adjustment operation command of radiofrequency ablation to the radiofrequency device to control the radiofrequency device to execute the real-time adjustment operation command of radiofrequency ablation and adjust the input parameters of radiofrequency ablation in real time.

[0137] like Figure 2 As shown, the second aspect of this embodiment provides a hardware system for implementing the radiofrequency ablation modulation method based on multi-parameter fusion and real-time feedback described in the first aspect of the embodiment, including:

[0138] The signal acquisition unit is used to acquire real-time physiological signal data, and perform feature calculations based on the real-time physiological signal data to obtain real-time physiological feature data, wherein the real-time physiological signal data includes real-time physiological temperature signal parameters and real-time physiological electrical signal parameters.

[0139] The water loss rate calculation unit is used to perform inversion calculation on the real-time physiological characteristic data, calculate the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter, and perform parameter fusion on the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter, so as to use the fused cell water loss rate parameter as the real-time cell water loss rate inversion value.

[0140] The damage assessment unit is used to obtain a preset tissue damage assessment prediction model. The real-time physiological characteristic data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment prediction model to calculate the tissue damage assessment value.

[0141] The feedback adjustment unit is used to acquire target adjustment conditions, judge the real-time physiological characteristic data, the real-time cell water loss rate inversion value and the tissue damage assessment value based on the target adjustment conditions, obtain the judgment result, and generate and feed back the corresponding radiofrequency ablation real-time adjustment operation command based on the judgment result to complete the real-time adjustment of radiofrequency ablation.

[0142] The working process, working details and technical effects of the system provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0143] like Figure 3 As shown, the third aspect of this embodiment provides an electronic device, including: a memory, a processor, and a transceiver that are sequentially and communicatively connected, wherein the memory is used to store a computer program, the transceiver is used to send and receive messages, and the processor is used to read the computer program and execute the radio frequency ablation adjustment method based on multi-parameter fusion and real-time feedback as described in the first aspect of the embodiment.

[0144] For specific examples, the memory may include, but is not limited to, random access memory (RAM), read-only memory (ROM), flash memory, first-in-first-out (FIFO) memory, and / or first-in-last-out (FILO) memory, etc.; specifically, the processor may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor may be implemented using at least one hardware form of DSP (Digital Signal Processing), FPGA (Field-Programmable Gate Array), PLA (Programmable Logic Array). The processor may also include a main processor and a coprocessor. The main processor, also known as the CPU (Central Processing Unit), is used to process data in the wake-up state; the coprocessor is a low-power processor used to process data in the standby state.

[0145] In some embodiments, the processor may integrate a GPU (Graphics Processing Unit), which is responsible for rendering and drawing the content to be displayed on the screen. For example, the processor may not be limited to microprocessors of the STM32F105 series, reduced instruction set computer (RISC) microprocessors, x86 architecture processors, or processors with integrated neural network processing units (NPUs). The transceiver may be, but is not limited to, a Wi-Fi transceiver, a Bluetooth transceiver, a General Packet Radio Service (GPRS) transceiver, a ZigBee (a low-power LAN protocol based on the IEEE 802.15.4 standard) transceiver, a 3G transceiver, a 4G transceiver, and / or a 5G transceiver. Furthermore, the device may also include, but is not limited to, a power module, a display screen, and other necessary components.

[0146] The working process, working details and technical effects of the electronic device provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0147] The fourth aspect of this embodiment provides a storage medium that stores instructions containing the radiofrequency ablation adjustment method based on multi-parameter fusion and real-time feedback as described in the first aspect of the embodiment. That is, the storage medium stores instructions that, when executed on a computer, perform the radiofrequency ablation adjustment method based on multi-parameter fusion and real-time feedback as described in the first aspect of the embodiment.

[0148] The storage medium refers to a carrier for storing data, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or memory sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0149] The working process, working details and technical effects of the storage medium provided in this embodiment can be found in the first aspect of the embodiment, and will not be repeated here.

[0150] The fifth aspect of this embodiment provides a computer program product containing instructions that, when executed on a computer, cause the computer to perform the radio frequency ablation adjustment method based on multi-parameter fusion and real-time feedback as described in the first aspect of the embodiment, wherein the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0151] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A radiofrequency ablation modulation system based on multi-parameter fusion and real-time feedback, characterized in that, Used to perform the following radiofrequency ablation modulation methods, including: Real-time physiological signal data is acquired, and feature calculations are performed based on the real-time physiological signal data to obtain real-time physiological feature data, wherein the real-time physiological signal data includes real-time physiological temperature signal parameters and real-time physiological electrical signal parameters; The real-time physiological characteristic data is inverted to calculate the first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter. The first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter are fused to use the fused cell water loss rate parameter as the real-time cell water loss rate inversion value. A preset tissue damage assessment and prediction model is obtained, and the real-time physiological characteristic data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment and prediction model so as to calculate the tissue damage assessment value through the tissue damage assessment and prediction model. The target adjustment conditions are obtained, and the real-time physiological characteristic data, the real-time cell water loss rate inversion value and the tissue damage assessment value are judged based on the target adjustment conditions to obtain the judgment result. The corresponding radiofrequency ablation real-time adjustment operation command is generated and fed back according to the judgment result to complete the real-time adjustment of radiofrequency ablation. Acquire real-time physiological signal data, and perform feature calculations based on the real-time physiological signal data to obtain real-time physiological feature data, including: Real-time physiological temperature signal parameters and real-time physiological electrical signal parameters are acquired and integrated into real-time physiological signal data. The real-time physiological temperature signal parameters include real-time tissue temperature, and the real-time physiological electrical signal parameters include real-time voltage RMS value, real-time current RMS value, and real-time impedance amplitude. The phase difference between the real-time voltage RMS value and the real-time current RMS value is calculated to obtain the real-time voltage-current phase difference. The real-time active power is calculated using the real-time effective voltage value, the real-time effective current value, and the real-time voltage-current phase difference. Obtain the preset sampling period and the tissue temperature at the previous moment. Calculate the tissue temperature change value based on the real-time tissue temperature and the tissue temperature at the previous moment. Use the ratio of the tissue temperature change value to the sampling period as the tissue temperature change rate at the current moment, and use the tissue temperature change rate at the current moment as the real-time tissue temperature change rate. Based on the real-time impedance amplitude, the real-time conductivity is calculated; The real-time impedance amplitude, the real-time voltage and current phase difference, the real-time active power, the real-time tissue temperature change rate, and the real-time conductivity are integrated to form real-time physiological characteristic data; The real-time physiological characteristic data are inverted to calculate the first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter, including: Obtain the preset tissue energy absorption coefficient and initial voltage-current phase difference. Based on the real-time voltage-current phase difference in the real-time physiological characteristic data, calculate the first cell water loss rate parameter using the following formula. : （1） in, The initial voltage and current phase difference, For the real-time voltage and current phase difference, This is the preset tissue energy absorption coefficient; Obtain the preset sensitivity coefficient and baseline impedance for the linear correlation between impedance and water loss rate. Based on the real-time impedance amplitude in the real-time physiological characteristic data, calculate the second cell water loss rate parameter using the following formula. : （2） in, Based on the fundamental impedance, This is the real-time impedance amplitude. The sensitivity coefficient is the linear correlation between impedance and water loss rate. Obtain the preset conductivity-water loss rate correlation attenuation coefficient and baseline conductivity. Based on the real-time conductivity in the real-time physiological characteristic data, calculate the third cell water loss rate parameter using the following formula. : （3） in, Based on the fundamental conductivity, For real-time conductivity, This is the attenuation coefficient related to conductivity and water loss rate; The first cell water loss rate parameter, the second cell water loss rate parameter, and the third cell water loss rate parameter are fused to obtain the fused cell water loss rate parameter as the real-time cell water loss rate inversion value, including: The preset sensitivity of the dynamic dominant weight switching of electrical parameters is obtained. Based on the real-time tissue temperature change rate in the real-time physiological characteristic data, the real-time dominant weight value of the electrical parameters at the current moment is calculated using the following formula. : （4） in, To monitor the rate of temperature change in real time, To dynamically control the sensitivity of weight switching, The base of the natural logarithm is given, and the electrical parameters include the first cell water loss rate parameter. and the second cell water loss parameter ; Obtain a preset first parameter confidence level and a second parameter confidence level, wherein the first parameter confidence level is used to represent the confidence level of the first cell water loss rate parameter. The degree of trust in the second parameter, the confidence level, is used to represent the level of trust in the second cell water loss parameter. The level of trust; Based on the dominant weight value of the real-time electrical parameter, the confidence level of the first parameter, and the confidence level of the second parameter, the weight of the first parameter is calculated using the following formula. Second parameter weight and the third parameter weight : （5） in, The first parameter is the confidence level. The confidence level is the second parameter. Using the first parameter weight The weight of the second parameter and the weight of the third parameter The water loss rate parameter of the first cell is calculated using the following formula. The second cell water loss parameter and the third cell water loss parameter Perform parameter fusion: （6） in, For parameters related to the water loss rate of fused cells; The water loss rate parameter of the fused cells generated by the fusion This value was determined as the inversion value of real-time cell water loss rate. A preset tissue damage assessment and prediction model is obtained. The real-time physiological characteristic data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment and prediction model to calculate the tissue damage assessment value, including: Obtain a preset tissue damage assessment and prediction model, wherein the tissue damage assessment and prediction model includes a thermal damage prediction model and an ablation depth prediction model; Obtain the total accumulated energy of the tissue at the previous moment, and calculate the total accumulated energy of the tissue at the current moment based on the total accumulated energy of the tissue at the previous moment and the real-time active power in the real-time physiological characteristic data; Using the aforementioned thermal damage prediction model, the predicted thermal damage value is calculated using the following formula. : （8） in, The preset molecular collision rate, As a pre-defined protein denaturation barrier, The gas constant is... For tissue temperature, Used to indicate the current time. Used to represent time variables A function used to represent the change of tissue temperature over time; Obtain a preset thermal damage determination threshold, and then use the predicted thermal damage value. Compare with the aforementioned thermal damage determination threshold; If the predicted thermal damage value If the thermal damage threshold is not exceeded, the determination result is that no thermal damage has occurred. If the predicted thermal damage value If the thermal damage threshold is exceeded, a determination result of thermal damage is obtained. The total energy accumulated in the tissue at the current moment and the real-time cell water loss rate inversion value are used as inputs to the ablation depth prediction model. The ablation depth prediction value is then calculated using the ablation depth prediction model according to the following formula. : （7） in, The preset tissue energy absorption coefficient, The preset water loss rate influencing factor, To accumulate total energy for the organization at this moment; The obtained determination result and the predicted ablation depth value Integrate the data to form an assessment value for tissue damage; Obtain target adjustment conditions, and use these conditions to evaluate the real-time physiological characteristic parameters, the real-time cell water loss rate inversion value, and the tissue damage assessment value to obtain evaluation results, including: Acquire target conditioning conditions, wherein the target conditioning conditions include tissue temperature conditioning conditions, cell water loss rate conditioning conditions, and tissue damage conditioning conditions; Based on the tissue temperature regulation conditions, the real-time tissue temperature is judged to obtain a first judgment result; Based on the cell water loss rate adjustment conditions, the real-time cell water loss rate inversion value is judged to obtain a second judgment result; Based on the tissue damage adjustment conditions, the tissue damage assessment value is judged to obtain a third judgment result; The first judgment result, the second judgment result, and the third judgment result are integrated to obtain a judgment result; Based on the judgment result, a corresponding real-time adjustment command for radiofrequency ablation is generated and fed back to complete the real-time adjustment of radiofrequency ablation, including: Obtain a preset adjustment operation table, and match the corresponding radiofrequency ablation real-time adjustment operation instruction in the adjustment operation table according to the judgment result; The radiofrequency ablation real-time adjustment operation command is fed back to the radiofrequency device to control the radiofrequency device to execute the radiofrequency ablation real-time adjustment operation command and adjust the input parameters of radiofrequency ablation in real time.

2. The radiofrequency ablation adjustment system based on multi-parameter fusion and real-time feedback according to claim 1, characterized in that, include: The signal acquisition unit is used to acquire real-time physiological signal data, and perform feature calculations based on the real-time physiological signal data to obtain real-time physiological feature data, wherein the real-time physiological signal data includes real-time physiological temperature signal parameters and real-time physiological electrical signal parameters. The water loss rate calculation unit is used to perform inversion calculation on the real-time physiological characteristic data, calculate the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter, and perform parameter fusion on the first cell water loss rate parameter, the second cell water loss rate parameter and the third cell water loss rate parameter, so as to use the fused cell water loss rate parameter as the real-time cell water loss rate inversion value. The damage assessment unit is used to obtain a preset tissue damage assessment prediction model. The real-time physiological characteristic data and the real-time cell water loss rate inversion value are used as inputs to the tissue damage assessment prediction model to calculate the tissue damage assessment value. The feedback adjustment unit is used to acquire target adjustment conditions, judge the real-time physiological characteristic data, the real-time cell water loss rate inversion value and the tissue damage assessment value based on the target adjustment conditions, obtain the judgment result, and generate and feed back the corresponding radiofrequency ablation real-time adjustment operation command based on the judgment result to complete the real-time adjustment of radiofrequency ablation.

3. An electronic device, characterized in that, The system includes a memory, a processor, and a transceiver that are sequentially and communicatively connected. The memory is used to store computer programs, the transceiver is used to send and receive messages, and the processor is used to read the computer programs and execute the radio frequency ablation modulation method performed by the radio frequency ablation modulation system based on multi-parameter fusion and real-time feedback as described in claim 1.

4. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or the instructions are executed by the computer, they implement the radiofrequency ablation adjustment method performed by the radiofrequency ablation adjustment system based on multi-parameter fusion and real-time feedback as described in claim 1.

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